Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Division of Dermatology, Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Comput Biol Med. 2024 Sep;179:108851. doi: 10.1016/j.compbiomed.2024.108851. Epub 2024 Jul 15.
In dermoscopic images, which allow visualization of surface skin structures not visible to the naked eye, lesion shape offers vital insights into skin diseases. In clinically practiced methods, asymmetric lesion shape is one of the criteria for diagnosing Melanoma. Initially, we labeled data for a non-annotated dataset with symmetrical information based on clinical assessments. Subsequently, we propose a supporting technique-a supervised learning image processing algorithm-to analyze the geometrical pattern of lesion shape, aiding non-experts in understanding the criteria of an asymmetric lesion. We then utilize a pre-trained convolutional neural network (CNN) to extract shape, color, and texture features from dermoscopic images for training a multiclass support vector machine (SVM) classifier, outperforming state-of-the-art methods from the literature. In the geometry-based experiment, we achieved a 99.00 % detection rate for dermatological asymmetric lesions. In the CNN-based experiment, the best performance is found 94 % Kappa Score, 95 % Macro F1-score, and 97 % weighted F1-score for classifying lesion shapes (Asymmetric, Half-Symmetric, and Symmetric).
在允许可视化肉眼不可见的表面皮肤结构的皮肤镜图像中，病变形状为皮肤疾病提供了重要的见解。在临床实践方法中，不对称的病变形状是诊断黑色素瘤的标准之一。最初，我们根据临床评估对无注释数据集的数据进行了具有对称信息的标记。随后，我们提出了一种支持技术——监督学习图像处理算法，以分析病变形状的几何模式，帮助非专业人士理解不对称病变的标准。然后，我们利用预先训练的卷积神经网络（CNN）从皮肤镜图像中提取形状、颜色和纹理特征，以训练多类支持向量机（SVM）分类器，优于文献中的最新方法。在基于几何的实验中，我们实现了对皮肤科不对称病变的 99.00%的检测率。在基于 CNN 的实验中，对于分类病变形状（不对称、半对称和对称），最佳性能是 94%的 Kappa 评分、95%的宏 F1 得分和 97%的加权 F1 得分。
